The invention relates to a method for nondestructive testing of pipes for surface flaws.
Nondestructive methods for testing metal pipes for surface flaws, such as the magnetic leakage flux test, are known since some time and have proven to be reliable.
The DC field leakage flux test for detecting discontinuities disposed on the interior wall of a pipe is used for pipes made of ferromagnetic steel for detecting, in particular, longitudinally extending discontinuities near the surface, such as tears, scales or bulges.
Disadvantageously, the conventional evaluation methods used for the signals are not always capable of unambiguously detecting discontinuities disposed on the interior surface of pipes, in particular under unfavorable conditions for the wall thickness of the pipe and depth of the interior flaw, when a predetermined magnetization field intensity is applied. The separation between the flaw-based signal and the noise level is then too small to arrive at meaningful results. It then becomes necessary to employ, as described below, novel filtering techniques based on wavelet algorithms.
Magnetic leakage flux signals are measured with inductive coils, Hall sensors or GMR sensors. These signals always include a certain amount of noise and have a slowly varying background. When using conventional noise reduction, the signal noise is reduced with an analog filtering technique and the slowly varying components are suppressed with a difference technique. The analog filtering technique quickly reaches its limits, because the flaw-based signals due to magnetic leakage flux often appear in a similar frequency range as the interfering signals from the background. There is also a risk that signals of interest, which should actually be displayed, are filtered out with difference techniques.
It would therefore be desirable to investigate alternative filtering techniques. In addition to digital filtering with conventional filtering algorithms, the so-called wavelet algorithms are particularly suited for this task. Instead of harmonic functions, wavelets are used as filter criteria because these can be very similar to the useful signals. With wavelet filters, noise can be much more effectively reduced than with conventional filtering techniques.
It is generally known, for example from DE 102 25 344 A1, to use a wavelet transformation for evaluating time-dependent signals in industrial process monitoring to separate the noise components of the signals from the information components of the signals. In a wavelet transformation, which is an extension of the Fourier transformation, the original signal is projected onto wavelet basic functions, which corresponds to a transformation from the time domain to the time-frequency plane. The wavelet functions which are localized in the time domain and in the frequency domain, are derived from a single prototype wavelet, the so-called mother function, by dilatation and translation.
The intent is here to significantly reduce with the wavelet transformation the noise level compared to the signal caused by the flaw.
The conventional method discloses in general terms the advantages of applying of the wavelet algorithm to noise suppression for monitoring industrial processes. It is imperative with pipes produced in a continuous production process that the signals from the nondestructive testing are analyzed in near-real-time, so as to be able to immediately change the production process when flaws occur (for example, correlating the flaw by marking the pipe section or stopping the production process). However, DE 102 25 344 A1 does not address this issue.
Therefore, a persistent problem in leakage flux testing is that surface test data of pipes must be measured and processed in near-real-time so as to allow intervention in the ongoing production process when flaws occur.
It is an object of the invention to provide a reliable and cost-effective method and a device for nondestructive testing of pipes using leakage flux, which can be used to measure and process the data related to surface flaws in the pipe in near-real-time by using a wavelet transformation.